{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tqdm\n",
    "import random\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.utils import *\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "\n",
    "import librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "\n",
    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=25)\n",
    "    #plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90, fontsize=15)\n",
    "    plt.yticks(tick_marks, classes, fontsize=15)\n",
    "\n",
    "    fmt = '.2f'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\", fontsize = 14)\n",
    "\n",
    "    plt.ylabel('True label', fontsize=20)\n",
    "    plt.xlabel('Predicted label', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2205, 6000)\n"
     ]
    },
    {
     "data": {
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       "      <td>2411.6</td>\n",
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       "      <td>2411.6</td>\n",
       "      <td>2411.6</td>\n",
       "      <td>2411.6</td>\n",
       "      <td>2411.6</td>\n",
       "      <td>2411.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>...</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.2</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.4</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.4</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
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       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>2409.6</td>\n",
       "      <td>...</td>\n",
       "      <td>2398.8</td>\n",
       "      <td>2398.2</td>\n",
       "      <td>2398.2</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
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       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2397.8</td>\n",
       "      <td>2395.8</td>\n",
       "      <td>...</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2383.8</td>\n",
       "      <td>2382.8</td>\n",
       "      <td>2382.8</td>\n",
       "      <td>...</td>\n",
       "      <td>2373.2</td>\n",
       "      <td>2372.8</td>\n",
       "      <td>2372.6</td>\n",
       "      <td>2372.4</td>\n",
       "      <td>2372.2</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>2372.0</td>\n",
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       "      <td>2372.0</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2370.0</td>\n",
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       "      <td>2369.8</td>\n",
       "      <td>2369.8</td>\n",
       "      <td>2369.8</td>\n",
       "      <td>2369.6</td>\n",
       "      <td>2369.6</td>\n",
       "      <td>2369.6</td>\n",
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       "<p>5 rows × 6000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0       1       2       3       4       5       6       7       8     \\\n",
       "0  2411.6  2411.6  2411.6  2411.6  2411.6  2411.6  2411.6  2411.6  2411.6   \n",
       "1  2409.6  2409.6  2409.6  2409.6  2409.6  2409.6  2409.6  2409.6  2409.6   \n",
       "2  2397.8  2397.8  2397.8  2397.8  2397.8  2397.8  2397.8  2397.8  2397.8   \n",
       "3  2383.8  2383.8  2383.8  2383.8  2383.8  2383.8  2383.8  2383.8  2382.8   \n",
       "4  2372.0  2372.0  2372.0  2372.0  2372.0  2372.0  2372.0  2372.0  2372.0   \n",
       "\n",
       "     9     ...    5990    5991    5992    5993    5994    5995    5996  \\\n",
       "0  2409.6  ...  2409.6  2409.2  2409.6  2409.4  2409.6  2409.4  2409.6   \n",
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       "2  2395.8  ...  2383.8  2383.8  2383.8  2383.8  2383.8  2383.8  2383.8   \n",
       "3  2382.8  ...  2373.2  2372.8  2372.6  2372.4  2372.2  2372.0  2372.0   \n",
       "4  2373.0  ...  2370.0  2370.0  2369.8  2369.8  2369.8  2369.8  2369.6   \n",
       "\n",
       "     5997    5998    5999  \n",
       "0  2409.6  2409.6  2409.6  \n",
       "1  2397.8  2397.8  2397.8  \n",
       "2  2383.8  2383.8  2383.8  \n",
       "3  2372.0  2372.0  2372.0  \n",
       "4  2369.6  2369.6  2369.6  \n",
       "\n",
       "[5 rows x 6000 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### READ AND CONCAT DATA ###\n",
    "\n",
    "label = pd.read_csv('profile.txt', sep='\\t', header=None)\n",
    "label.columns = ['Cooler','Valve','Pump','Accumulator','Flag']\n",
    "\n",
    "df = pd.read_csv('EPS1.txt', sep='\\t', header=None)\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT ORIGINAL MOTOR POWER ###\n",
    "\n",
    "df.iloc[2,:].plot(figsize=(8,5), title='Original Motor Power')\n",
    "plt.ylabel('Motor Power (in W)'); plt.xlabel('Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2205, 5999)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### APPLY CLIPPING AND FIRST ORDER DIFFERECE ###\n",
    "\n",
    "df = df.values\n",
    "df = np.clip(np.diff(df, axis=1), -5,5)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT STANDARDIZED DATA ###\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.plot(df[2])\n",
    "plt.title('Standardized Motor Power')\n",
    "plt.ylabel('First Difference'); plt.xlabel('Time')\n",
    "np.set_printoptions(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1449\n",
       "1     756\n",
       "Name: Flag, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### LABEL DISTRIBUTION ###\n",
    "\n",
    "label = label.Flag\n",
    "label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### LABEL ENCODING ###\n",
    "\n",
    "diz_label = {0:'stable', 1:'not stable'}\n",
    "y = to_categorical(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 2205/2205 [00:09<00:00, 222.94it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2205, 12, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### APPLY MFCC TRANSFORMATION FOR EACH SIGNAL ###\n",
    "\n",
    "df_mfcc = []\n",
    "\n",
    "for i,sample in enumerate(tqdm.tqdm(df)):\n",
    "    \n",
    "    sample_mfcc = librosa.feature.mfcc(np.asfortranarray(sample),sr=40000)\n",
    "    df_mfcc.append(sample_mfcc)\n",
    "    \n",
    "df_spectre = np.asarray(df_mfcc)\n",
    "df_spectre = df_spectre.transpose(0,2,1)\n",
    "df_spectre.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT MFCC FOR A SINGLE SIGNAL ###\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "plt.plot(df_spectre[2])\n",
    "plt.legend(['mfcc_'+str(i) for i in range(df_spectre.shape[-1])], \n",
    "          loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.title('MFCC features')\n",
    "plt.ylabel('Amplitudes'); plt.xlabel('Time')\n",
    "\n",
    "np.set_printoptions(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TRAIN TEST SPLIT ###\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_spectre, y, random_state = 42, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE RESNET CNN ###\n",
    "\n",
    "def set_seed(seed):\n",
    "    \n",
    "    tf.random.set_seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    \n",
    "\n",
    "def get_model(data):\n",
    "    \n",
    "    set_seed(33)\n",
    "        \n",
    "    def residual_block(init, hidden_dim):\n",
    "        \n",
    "        init = Conv1D(hidden_dim, 3, activation='relu', padding=\"same\")(init)\n",
    "        \n",
    "        x = Conv1D(hidden_dim, 3, activation='relu', padding=\"same\")(init)\n",
    "        x = Conv1D(hidden_dim, 3, activation='relu', padding=\"same\")(x)\n",
    "        x = Conv1D(hidden_dim, 3, activation='relu', padding=\"same\")(x)\n",
    "        skip = Add()([x, init])\n",
    "        \n",
    "        return skip\n",
    "    \n",
    "    inp = Input(shape=(data.shape[1], data.shape[2]))\n",
    "    \n",
    "    x = residual_block(inp, 256)\n",
    "    x = residual_block(x, 128)\n",
    "    x = residual_block(x, 32)\n",
    "    x = GlobalMaxPool1D()(x)\n",
    "    \n",
    "    out = Dense(len(diz_label), activation='softmax')(x)\n",
    "    \n",
    "    model = Model(inputs=inp, outputs=out)\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "7/7 - 3s - loss: 6.6746 - accuracy: 0.5564 - val_loss: 1.6293 - val_accuracy: 0.4407\n",
      "Epoch 2/100\n",
      "7/7 - 3s - loss: 2.0541 - accuracy: 0.5532 - val_loss: 1.5703 - val_accuracy: 0.6215\n",
      "Epoch 3/100\n",
      "7/7 - 3s - loss: 0.8552 - accuracy: 0.6068 - val_loss: 0.5724 - val_accuracy: 0.7345\n",
      "Epoch 4/100\n",
      "7/7 - 3s - loss: 0.6669 - accuracy: 0.6194 - val_loss: 0.5776 - val_accuracy: 0.6497\n",
      "Epoch 5/100\n",
      "7/7 - 3s - loss: 0.5928 - accuracy: 0.6660 - val_loss: 0.6016 - val_accuracy: 0.6723\n",
      "Epoch 6/100\n",
      "7/7 - 3s - loss: 0.5572 - accuracy: 0.6862 - val_loss: 0.6777 - val_accuracy: 0.6384\n",
      "Epoch 7/100\n",
      "7/7 - 3s - loss: 0.5585 - accuracy: 0.6849 - val_loss: 0.5978 - val_accuracy: 0.6610\n",
      "Epoch 8/100\n",
      "7/7 - 3s - loss: 0.5307 - accuracy: 0.7095 - val_loss: 0.5675 - val_accuracy: 0.6836\n",
      "Epoch 9/100\n",
      "7/7 - 3s - loss: 0.5306 - accuracy: 0.7297 - val_loss: 0.5477 - val_accuracy: 0.7288\n",
      "Epoch 10/100\n",
      "7/7 - 3s - loss: 0.5422 - accuracy: 0.7196 - val_loss: 0.5575 - val_accuracy: 0.6780\n",
      "Epoch 11/100\n",
      "7/7 - 3s - loss: 0.5208 - accuracy: 0.7347 - val_loss: 0.5380 - val_accuracy: 0.7175\n",
      "Epoch 12/100\n",
      "7/7 - 3s - loss: 0.5046 - accuracy: 0.7335 - val_loss: 0.5212 - val_accuracy: 0.7514\n",
      "Epoch 13/100\n",
      "7/7 - 3s - loss: 0.4927 - accuracy: 0.7555 - val_loss: 0.5913 - val_accuracy: 0.6667\n",
      "Epoch 14/100\n",
      "7/7 - 3s - loss: 0.5566 - accuracy: 0.7284 - val_loss: 0.5501 - val_accuracy: 0.6893\n",
      "Epoch 15/100\n",
      "7/7 - 3s - loss: 0.5431 - accuracy: 0.7146 - val_loss: 0.5618 - val_accuracy: 0.6667\n",
      "Epoch 16/100\n",
      "7/7 - 3s - loss: 0.5038 - accuracy: 0.7341 - val_loss: 0.5237 - val_accuracy: 0.7627\n",
      "Epoch 17/100\n",
      "7/7 - 3s - loss: 0.5042 - accuracy: 0.7442 - val_loss: 0.6732 - val_accuracy: 0.6497\n",
      "Epoch 18/100\n",
      "7/7 - 3s - loss: 0.5394 - accuracy: 0.7101 - val_loss: 0.5092 - val_accuracy: 0.7853\n",
      "Epoch 19/100\n",
      "7/7 - 3s - loss: 0.5087 - accuracy: 0.7391 - val_loss: 0.4961 - val_accuracy: 0.7627\n",
      "Epoch 20/100\n",
      "7/7 - 3s - loss: 0.4743 - accuracy: 0.7813 - val_loss: 0.4965 - val_accuracy: 0.7684\n",
      "Epoch 21/100\n",
      "7/7 - 3s - loss: 0.4593 - accuracy: 0.7776 - val_loss: 0.5342 - val_accuracy: 0.7006\n",
      "Epoch 22/100\n",
      "7/7 - 3s - loss: 0.4598 - accuracy: 0.7832 - val_loss: 0.4815 - val_accuracy: 0.7740\n",
      "Epoch 23/100\n",
      "7/7 - 3s - loss: 0.4779 - accuracy: 0.7744 - val_loss: 0.4730 - val_accuracy: 0.7797\n",
      "Epoch 24/100\n",
      "7/7 - 3s - loss: 0.4357 - accuracy: 0.8110 - val_loss: 0.5573 - val_accuracy: 0.6780\n",
      "Epoch 25/100\n",
      "7/7 - 3s - loss: 0.4524 - accuracy: 0.7933 - val_loss: 0.4632 - val_accuracy: 0.7853\n",
      "Epoch 26/100\n",
      "7/7 - 3s - loss: 0.4340 - accuracy: 0.8103 - val_loss: 0.4835 - val_accuracy: 0.7571\n",
      "Epoch 27/100\n",
      "7/7 - 3s - loss: 0.4398 - accuracy: 0.7807 - val_loss: 0.5869 - val_accuracy: 0.6554\n",
      "Epoch 28/100\n",
      "7/7 - 3s - loss: 0.5345 - accuracy: 0.7108 - val_loss: 0.6164 - val_accuracy: 0.6610\n",
      "Epoch 29/100\n",
      "7/7 - 3s - loss: 0.4692 - accuracy: 0.7631 - val_loss: 0.4716 - val_accuracy: 0.7740\n",
      "Epoch 30/100\n",
      "7/7 - 3s - loss: 0.4340 - accuracy: 0.7820 - val_loss: 0.4491 - val_accuracy: 0.7910\n",
      "Epoch 31/100\n",
      "7/7 - 3s - loss: 0.4156 - accuracy: 0.8135 - val_loss: 0.4835 - val_accuracy: 0.7458\n",
      "Epoch 32/100\n",
      "7/7 - 3s - loss: 0.4200 - accuracy: 0.8103 - val_loss: 0.4508 - val_accuracy: 0.8023\n",
      "Epoch 33/100\n",
      "7/7 - 3s - loss: 0.4157 - accuracy: 0.8028 - val_loss: 0.4704 - val_accuracy: 0.7345\n",
      "Epoch 34/100\n",
      "7/7 - 3s - loss: 0.4100 - accuracy: 0.8349 - val_loss: 0.6406 - val_accuracy: 0.6441\n",
      "Epoch 35/100\n",
      "7/7 - 3s - loss: 0.5015 - accuracy: 0.7492 - val_loss: 0.5491 - val_accuracy: 0.7119\n",
      "Epoch 36/100\n",
      "7/7 - 3s - loss: 0.4598 - accuracy: 0.7769 - val_loss: 0.4516 - val_accuracy: 0.7966\n",
      "Epoch 37/100\n",
      "7/7 - 3s - loss: 0.4115 - accuracy: 0.8166 - val_loss: 0.4102 - val_accuracy: 0.8079\n",
      "Epoch 38/100\n",
      "7/7 - 3s - loss: 0.3945 - accuracy: 0.8299 - val_loss: 0.4507 - val_accuracy: 0.7797\n",
      "Epoch 39/100\n",
      "7/7 - 3s - loss: 0.3806 - accuracy: 0.8368 - val_loss: 0.3934 - val_accuracy: 0.8192\n",
      "Epoch 40/100\n",
      "7/7 - 3s - loss: 0.3622 - accuracy: 0.8551 - val_loss: 0.4374 - val_accuracy: 0.7684\n",
      "Epoch 41/100\n",
      "7/7 - 3s - loss: 0.3742 - accuracy: 0.8406 - val_loss: 0.4109 - val_accuracy: 0.8136\n",
      "Epoch 42/100\n",
      "7/7 - 3s - loss: 0.3643 - accuracy: 0.8488 - val_loss: 0.4468 - val_accuracy: 0.7684\n",
      "Epoch 43/100\n",
      "7/7 - 3s - loss: 0.3675 - accuracy: 0.8399 - val_loss: 0.4131 - val_accuracy: 0.8079\n",
      "Epoch 44/100\n",
      "7/7 - 3s - loss: 0.3679 - accuracy: 0.8393 - val_loss: 0.4044 - val_accuracy: 0.8079\n",
      "Epoch 45/100\n",
      "7/7 - 3s - loss: 0.3712 - accuracy: 0.8355 - val_loss: 0.3737 - val_accuracy: 0.8418\n",
      "Epoch 46/100\n",
      "7/7 - 3s - loss: 0.3249 - accuracy: 0.8589 - val_loss: 0.3520 - val_accuracy: 0.8701\n",
      "Epoch 47/100\n",
      "7/7 - 3s - loss: 0.3230 - accuracy: 0.8670 - val_loss: 0.3523 - val_accuracy: 0.8531\n",
      "Epoch 48/100\n",
      "7/7 - 3s - loss: 0.3170 - accuracy: 0.8765 - val_loss: 0.4018 - val_accuracy: 0.8023\n",
      "Epoch 49/100\n",
      "7/7 - 3s - loss: 0.3219 - accuracy: 0.8652 - val_loss: 0.4916 - val_accuracy: 0.7401\n",
      "Epoch 50/100\n",
      "7/7 - 3s - loss: 0.4710 - accuracy: 0.7713 - val_loss: 0.4489 - val_accuracy: 0.7514\n",
      "Epoch 51/100\n",
      "7/7 - 3s - loss: 0.3903 - accuracy: 0.8185 - val_loss: 0.3835 - val_accuracy: 0.8249\n",
      "Epoch 52/100\n",
      "7/7 - 3s - loss: 0.3565 - accuracy: 0.8381 - val_loss: 0.3421 - val_accuracy: 0.8644\n",
      "Epoch 53/100\n",
      "7/7 - 4s - loss: 0.3001 - accuracy: 0.8715 - val_loss: 0.3474 - val_accuracy: 0.8475\n",
      "Epoch 54/100\n",
      "7/7 - 3s - loss: 0.3049 - accuracy: 0.8815 - val_loss: 0.3574 - val_accuracy: 0.8362\n",
      "Epoch 55/100\n",
      "7/7 - 3s - loss: 0.2961 - accuracy: 0.8740 - val_loss: 0.6189 - val_accuracy: 0.6949\n",
      "Epoch 56/100\n",
      "7/7 - 3s - loss: 0.3917 - accuracy: 0.8122 - val_loss: 0.4189 - val_accuracy: 0.8079\n",
      "Epoch 57/100\n",
      "7/7 - 3s - loss: 0.3307 - accuracy: 0.8431 - val_loss: 0.4471 - val_accuracy: 0.7966\n",
      "Epoch 58/100\n",
      "7/7 - 4s - loss: 0.3287 - accuracy: 0.8469 - val_loss: 0.3976 - val_accuracy: 0.8136\n",
      "Epoch 59/100\n",
      "7/7 - 3s - loss: 0.3270 - accuracy: 0.8425 - val_loss: 0.3095 - val_accuracy: 0.8588\n",
      "Epoch 60/100\n",
      "7/7 - 3s - loss: 0.2700 - accuracy: 0.8803 - val_loss: 0.3910 - val_accuracy: 0.8305\n",
      "Epoch 61/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "7/7 - 3s - loss: 0.2694 - accuracy: 0.8885 - val_loss: 0.3155 - val_accuracy: 0.8588\n",
      "Epoch 00061: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b85ea88160>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es = EarlyStopping(monitor='val_accuracy', mode='auto', restore_best_weights=True, verbose=1, patience=15)\n",
    "\n",
    "model = get_model(X_train)\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=256, verbose=2, validation_split=0.1, callbacks=[es])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### GET PREDICTED CLASS ON TEST ###\n",
    "\n",
    "pred_test = np.argmax(model.predict(X_test), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  not stable       0.72      0.72      0.72       147\n",
      "      stable       0.86      0.86      0.86       294\n",
      "\n",
      "    accuracy                           0.81       441\n",
      "   macro avg       0.79      0.79      0.79       441\n",
      "weighted avg       0.81      0.81      0.81       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report([diz_label[np.argmax(label)] for label in y_test], \n",
    "                            [diz_label[label] for label in pred_test]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix([diz_label[np.argmax(label)] for label in y_test], \n",
    "                              [diz_label[label] for label in pred_test])\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plot_confusion_matrix(cnf_matrix, classes=list(diz_label.values()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### DUMMY CLASSIFIER ACCURACY (always stable) ###\n",
    "\n",
    "sum([diz_label[np.argmax(label)] == 'stable' for label in y_test]) / len(pred_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow2]",
   "language": "python",
   "name": "conda-env-tensorflow2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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